88 citations · 280 across the 11 of their papers we have counts for
11 papers
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Liyuan Wang, Xingxing Zhang, Qian Li +4
Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world. For this purpose, a desirable solution should properly balance memory st…
A Comprehensive Survey of Continual Learning: Theory, Method and Application
Liyuan Wang, Xingxing Zhang, Hang Su +1
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as co…
Momentum Calibration for Text Generation
Xingxing Zhang, Yiran Liu, Xun Wang +5
The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as…
CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One
Liyuan Wang, Xingxing Zhang, Qian Li +2
Continual learning requires incremental compatibility with a sequence of tasks. However, the design of model architecture remains an open question: In general, learning all tasks w…
Diagnosing Ensemble Few-Shot Classifiers
Weikai Yang, Xi Ye, Xingxing Zhang +6
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance. When the performance is not satisfactory, it is usually diffi…
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization
Ruipeng Jia, Xingxing Zhang, Yanan Cao +3
In zero-shot multilingual extractive text summarization, a model is typically trained on English summarization dataset and then applied on summarization datasets of other languages…
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization
Xingxing Zhang, Furu Wei, Ming Zhou
Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically…
Neural Latent Extractive Document Summarization
Xingxing Zhang, Mirella Lapata, Furu Wei +1
Extractive summarization models require sentence-level labels, which are usually created heuristically (e.g., with rule-based methods) given that most summarization datasets only h…
Sentence Simplification with Deep Reinforcement Learning
Xingxing Zhang, Mirella Lapata
Sentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from…
Dependency Parsing as Head Selection
Xingxing Zhang, Jianpeng Cheng, Mirella Lapata
Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently…
Top-down Tree Long Short-Term Memory Networks
Xingxing Zhang, Liang Lu, Mirella Lapata
Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have been successfully applied to a variety of sequence modeling…